Robotics in the Construction Industry: A Bibliometric Review of Recent Trends and Technological Evolution

Abstract

The construction industry faces persistent challenges, including labor shortages and safety hazards, while traditional construction methods are increasingly strained by the complexity and sustainability demands of modern projects. The integration of robotics shows significant potential for mitigating labor shortages and enhancing safety on construction sites. The current adoption of robotics technologies is driven by both the maturity of robotics technology and the potential for cost reduction compared with manual labor. This review synthesizes recent advancements and trends in construction robotics through a bibliometric analysis of 212 publications indexed in Web of Science from 2002 to 2024. Key findings indicate a 320% increase in research output from 2015 to 2022, with dominant clusters focusing on autonomous navigation, human–robot collaboration, and sustainability-driven automation. Geographically, China and the United States lead in number of publications, with 67 and 65 articles, respectively; however, cross-border collaborations remain sparse, constituting fewer than 5% of co-authored papers. Keyword co-occurrence analysis reveals evolving priorities, including artificial intelligence (AI)-driven adaptive control, modular prefabrication, and the ethical implications of automation. Despite technological advancements, critical gaps remain in terms of interoperability, workforce retraining, and regulatory frameworks. This study emphasizes the need for interdisciplinary integration, standardized protocols, and policy alignment to bridge the divide between academic innovation and industry adoption, ultimately facilitating the global transition toward Construction 4.0.

1. Introduction

The integration of robotics into the construction industry has garnered increasing research interest, driven by the need to address persistent challenges such as labor shortages, safety hazards, and material inefficiencies [1]. While traditional construction methods heavily rely on manual labor and fragmented workflows, recent advancements in robotics, artificial intelligence (AI), and automation offer transformative potential. Emerging technologies, including autonomous navigation systems, AI-driven modular prefabrication, and human–robot collaboration frameworks, have demonstrated capabilities in tasks ranging from 3D printing to real-time site monitoring [2,3,4].

This research review examines the recent literature on robotics in the construction industry, emphasizing both practical applications and emerging speculative advancements. Several studies have provided systematic reviews of the state of robotic adoption and identify emerging trends. For instance, Parascho [5] explored the transition from automation to collaborative robotics, while Chu et al. [6] assessed the growing role of robotics across various global regions. Similarly, Ayoola et al. [7] concentrated on real-world applications, surveying the contributions of robotics to enhancing safety and productivity at construction sites. Additionally, other studies, such as that by Thaker [8], specifically investigated the role of AI-driven methodologies, including reinforcement learning, in adapting robotic systems to unstructured construction environments.

A substantial body of research concentrates on construction robotics that utilize modular and prefabrication techniques. Chea et al. [9] reviewed the role of automation in structural prefabrication, examining the contributions of robotics to design, manufacturing, and assembly processes. Similarly, Külz et al. [10] introduced a modular robotic framework that incorporates BIM for the autonomous reconfiguration and execution of construction tasks. Furthermore, Zhu et al. [11] expanded upon this by employing deep reinforcement learning for robotic assembly planning, showcasing advancements in the optimization of automated workflows for prefabricated structures.

In addition to modular construction, several studies have investigated the application of robotics in specific areas of construction. Gharbia et al. [12] conducted a systematic review of robotic technologies utilized in concrete building construction with a particular focus on 3D printing applications. Bogue [13] examined various automated construction systems, including robots for bricklaying and glazing, drone-based surveying, and robotic 3D printing. Meanwhile, Leng et al. [14] offered an alternative perspective by exploring human–robot interaction in construction, highlighting the potential for hybrid manual–robotic workflows to improve efficiency. While automation trends continue to progress, barriers to their adoption remain. Studies such as those by Delgado et al. [15] and Thaker [8] have discussed the economic, technical, and cultural constraints slowing the uptake of robotics in construction. Similarly, Olofsson and Emborg [16] analyzed adoption feasibility through industry surveys, highlighting factors such as high initial costs and workforce skill gaps. Some studies have proposed frameworks to overcome these barriers. For instance, Borrmann et al. [17] introduced the concept of a cyber–physical “information backbone” to improve coordination among robot fleets and human operators.

Despite significant advancements, research gaps remain in areas such as swarm robotics, intelligent automation scheduling, and the real-world implementation of AI-driven robotics. While studies by Külz et al. [10] and Borrmann et al. [17] have suggested pathways toward cyber–physical integration and fleet-based automation, experimental validation of such systems remains limited. Additionally, while reinforcement learning and AI models for robotic control have been increasingly explored [11], their practical deployment on dynamic construction sites continues to be a challenge.

However, the existing literature exhibits critical limitations. First, prior reviews have predominantly focused on regional applications, lacking a global synthesis of trends. Second, studies often isolate technical advancements from their socio-economic implications, such as workforce retraining or ethical governance. Third, despite the proliferation of bibliometric analyses in adjacent fields, construction robotics remains understudied through a systematic, data-driven lens.

In summary, the current research illustrates the transformative potential of robotics in construction for modular prefabrication, AI-driven decision making, and human–robot collaboration [18]. However, widespread integration depends on overcoming technical, economic, and regulatory challenges. Future research must address these obstacles while refining experimental implementations of emerging robotic technologies, bridging the gap between conceptual innovation and industry adoption [19].

The integration of robotics in the construction industry is advancing through practical applications such as modular prefabrication, 3D printing, and autonomous site management, supported by AI, BIM, and sensor-enabled systems, while future opportunities include collaborative human–robot systems and swarm robotics [20]. Despite significant advances in robotics for the construction industry, several research gaps and unresolved challenges remain. Starting from the above problems, this study tries to interpret the current trends in and the future development of construction robots using the bibliometric analysis method [21]. With this goal, this review aims to achieve the following: (1) to map global research trends in construction robotics (2002–2024) through bibliometric analysis, identifying dominant clusters and collaboration patterns; and (2) to quantify interdisciplinary linkages between robotics, AI, and socio-technical domains. Through integrating fragmented insights into a cohesive framework, this work contributes to the transition toward Construction 4.0, where intelligent automation aligns with sustainable and inclusive industry practices.

2. Data and Methods

2.1. Methodological Rationale

To address the research objectives, we evaluated multiple methodologies commonly employed in literature reviews, including a systematic literature review (SLR), content analysis, and bibliometric analysis. While SLRs emphasize qualitative synthesis through predefined protocols, they often struggle to map large-scale trends or collaboration patterns quantitatively [22]. Content analysis, though effective for thematic categorization, lacks tools to visualize interdisciplinary linkages or temporal evolution. In contrast, bibliometric analysis leverages quantitative metrics to objectively identify research frontiers, collaboration dynamics, and knowledge clusters within a field. This method is uniquely suited to our goal of mapping global trends and quantifying interdisciplinary integration in construction robotics for the following three reasons: (1) Scalability: Bibliometrics allows for the efficient processing of large datasets to uncover patterns invisible to manual reviews, such as the dominance of specific institutions or sudden shifts in research focus. (2) Dynamic visualization: Tools such as CiteSpace enable time-sliced network analysis, highlighting how clusters like “human–robot collaboration” evolved from theoretical proposals to applied frameworks. (3) Reproducibility: Algorithm-driven approaches reduce subjective bias, aligning with our aim to provide an objective, data-driven synthesis [23].

2.2. Data Sources

In this study, we conducted a comprehensive bibliometric analysis using data extracted from the Web of Science Core Collection (WoSCC), a globally recognized citation database. The search strategy was designed to capture peer-reviewed literature on construction robotics, focusing on journal articles, reviews, conference proceedings, and early-access publications. The literature in the English language includes journals indexed in SCI, SSCI, AHCI, CPCI-S, and CPCI-SSH, among others. The search query for the research used the following keywords: “construction robot*” OR “building robot*” OR “construction robotic*” OR “building robotic*” OR “robotic* in construction” OR “robotic* for construction” OR “robotic* in building” OR “robotic* for building”. The search spanned publications from 2002 to 2024 to track both foundational and emerging trends. After removing duplicates, editorials, and non-English documents, 212 relevant articles were retained for analysis.

2.3. Data Screening and Preprocessing

To ensure data relevance and accuracy, the following three-step screening protocol was applied: (1) Initial filtering: Non-technical publications (e.g., opinion pieces, news articles) were excluded [24]. (2) Manual verification: Two independent researchers cross-checked titles, abstracts, and keywords to eliminate studies unrelated to construction robotics (e.g., studies about industrial robots in manufacturing). (3) Normalization: Variations of keywords and institutional affiliations were standardized to minimize redundancy [25].

2.4. Analytical Tools and Techniques

The analysis leveraged CiteSpace 6.3.R1 to generate visual networks and quantify research trends as follows: (1) Collaboration networks: Co-authorship maps were constructed for authors, institutions, and countries using a clustering algorithm. (2) Keyword analysis: Keyword co-occurrence and timeline networks were generated using Pathfinder pruning to highlight thematic evolution. (3) Burst detection: CiteSpace’s Kleinberg’s algorithm identified sudden increases in keyword usage [26].

3. Analysis and Results

3.1. Visualization Analysis of Researchers

Highly productive authors are the core driving force behind the research in this field, while highly cited authors represent those with significant influence within the domain. Co-authors reveal the collaborative networks among researchers and highlight the research connections within specific fields [27]. According to the bibliometric data, a total of 307 authors have contributed to the literature in this domain, as shown in

Table 1. Top 10 researchers by number of publications.

No.	Name	Institution	Publication Volume
1	Kamat, Vineet R.	University of Michigan	18
2	Bock, Thomas	Technical University of Munich	8
3	Menassa, Carol C.	University of Michigan	8
4	Wang, Xi	Texas A & M University System	5
4	Pan, Mi	University of Macau	5
4	Liu, Yizhi	Hunan University of Science & Technology	5
4	Pan, Wei	University of Hong Kong	5
4	Jebelli, Houtan	University of Illinois Urbana–Champaign	5

. The authors with the highest publication output are Kamat, Vineet R., Bock, Thomas, Menassa, Carol C., and Wang, Xi. Kamat, Vineet R. (University of Michigan) has published 18 articles, ranking first in publication output, indicating their active involvement in construction robot research and their role as a major academic leader in this field. Such a high number of publications suggests that they are involved in various research topics, and that their work plays a central role in advancing the field. Bock, Thomas (Technical University of Munich) and Menassa, Carol C. (University of Michigan) have published eight articles each, ranking second and third. Their significant publication output indicates that they also make substantial academic contributions in this field. Moreover, their close collaboration with Kamat, Vineet R., particularly the academic partnership between Menassa and Kamat at the University of Michigan, suggests a strong research team. Wang, Xi (Texas A & M University System), Pan, Mi (University of Macau), and Liu, Yizhi (Hunan University of Science & Technology) have published five articles each, with their research making a considerable impact in specific regions or research topics. In particular, Pan, Mi’s affiliation with the University of Macau suggests that the institution may serve as a research hub, advancing the development of construction robotics in the region.

As shown in Figure 1. As shown by the analysis of the number of publications and collaborative networks, Kamat, Vineet R. and Bock, Thomas remain central figures in the field. Kamat’s in-depth research in construction robotics has likely earned them greater academic authority, while Menassa’s collaboration provides strong support for the academic development of the field. The collaboration network analysis reveals that scholars such as Wang, Xi, Pan, Wei, and Jebelli, Houtan have more extensive international networks, with their affiliations in different countries offering opportunities for global academic collaboration. This international collaboration trend is likely to propel the global development of construction robot technology.

Table 2. Top 10 most cited research papers.

No.	Author	Year	Journal	Title	Citation
1	Bock, T.	2015	Automation in Construction	“The future of construction automation: Technological disruption and the upcoming ubiquity of robotics”	265
2	Scassellati, B.	2022	Autonomous Robots	“Theory of mind for a humanoid robot”	200
3	Goh, M. and Goh, Y.M.	2019	Automation in Construction	“Lean production theory-based simulation of modular construction processes”	135
4	Beane, M.	2019	Administrative Science Quarterly	“Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail”	134
5	Wang, Z.L., Li, H., and Zhang, X.L.	2019	Automation in Construction	“Construction waste recycling robot for nails and screws: Computer vision technology and neural network approach”	130
6	Robertson, J.	2010	Body & Society	“Gendering Humanoid Robots: Robo-Sexism in Japan”	122
7	Sacks, R., Girolami, M., and Brilakis, I.	2020	Developments in the Built Environment	“Building Information Modelling, Artificial Intelligence and Construction Tech”	118
8	Billard, A., Robins, B., (…), Dautenhahn, K.	2007	Assistive Technology	“Building Robota, a mini-humanoid robot for the rehabilitation of children with autism”	115
9	Adami, P., Rodrigues, P.B., (…), Lucas, G.	2021	Advanced Engineering Informatics	“Effectiveness of VR-based training on improving construction workers’ knowledge, skills, and safety behavior in robotic teleoperation”	105
10	Liu, Y.Z., Habibnezhad, M., and Jebelli, H.	2021	Automation in Construction	“Brainwave-driven human–robot collaboration in construction”	93

presents the ten most cited articles. The most cited article is by Bock, T. (2015), is titled “The future of construction automation: Technological disruption and the upcoming ubiquity of robotics”, and has been cited 265 times. The citation count for this article is significantly higher than for others, indicating its high level of academic influence in the field of construction robotics. Bock’s discussion of the future of construction automation and the increasing prevalence of robotics has become a seminal work in the field, providing a theoretical foundation for future research. The article offers a deep exploration of the potential technological revolution in construction robotics, setting the stage for subsequent studies [28]. Scassellati, B.’s work (2022) “Theory of mind for a humanoid robot” has been cited 200 times. This paper explores the concept of “Theory of Mind” in robotics, particularly how humanoid robots can be endowed with the ability to understand and simulate human mental states. Although focused on humanoid robots, it provides valuable insights for construction robotics, especially in enhancing collaboration efficiency between robots and humans [29]. Goh, M. and Goh, Y.M. (2019) published “Lean production theory-based simulation of modular construction processes”, which has been cited 135 times. This paper, based on lean production theory, explores the application of construction robots in modular construction processes through simulation, proposing effective ways to optimize construction processes using robots, particularly in enhancing production efficiency [29].

These highly cited works reveal a strong interdisciplinary collaboration in construction robotics, incorporating fields such as artificial intelligence, virtual reality, and environmental sustainability [30]. For instance, the research by Wang, Z.L. and Li, H. combines computer vision with construction robots, while Adami, P. introduces virtual reality for training purposes. As construction robotics continues to evolve, collaboration among scholars is expected to deepen. Future research will likely focus on applied research in construction robotics, exploring how robots can be effectively implemented in the construction industry. Moreover, interdisciplinary and inter-regional collaboration will be pivotal to the future development of this field [31].

Meanwhile, these results also reflect industry–academia collaboration and commercialization. For example, Built Robotics (USA) focuses on the deployment of autonomous excavators in infrastructure projects, reducing labor costs by 30%. The China State Construction Engineering Corporation (CSCEC) has adopted 3D-printing robots for prefabricated housing, achieving a 50% reduction in material waste. Komatsu’s Smart Construction Initiative has resulted in the integration of AI-guided bulldozers and drones in Japan, improving site efficiency by 40%.

3.2. Visualization Analysis of Research Institutions

As shown in

Table 3. Top 10 research institutions by number of publications.

No.	Institution	Publication Volume	No.	Institution	Publication Volume
1	University of Michigan	15	6	Swiss Federal Institutes of Technology Domain	7
2	University of Michigan System	15	7	Pennsylvania State University	6
3	University of Hong Kong	10	7	Hong Kong Polytechnic University	6
4	Hanyang University	8	7	ETH Zurich	6
5	Technical University of Munich	7	7	Pennsylvania Commonwealth System of Higher Education (PCSHE)	6

. The institutions with the largest number of collaborative publications are the University of Michigan and the University of Michigan System, each with 15 published articles. This indicates that both institutions are highly active in the field of construction robotics and hold significant positions within the domain. In particular, the University of Michigan, as the top-ranking institution, has undoubtedly played a central role in advancing construction robotics technology and related fields. As shown in Figure 2. From the co-authorship network, it is clear that there is a strong collaboration between the University of Michigan and the University of Michigan System, suggesting that they have a close working relationship and jointly contribute to the progression of related research.

The University of Hong Kong, with 10 publications, ranks third. This institution is also highly active in the construction robotics field, with especially significant influence in the Asian region. The University of Hong Kong collaborates extensively with other Asian universities, such as Hong Kong Polytechnic University, which is likely related to its research direction in the application of construction robotics, particularly in the context of the Hong Kong construction industry and its surrounding areas.

Hanyang University, with eight publications, ranks fourth. The research from this institution holds an important place in the construction robotics field in Korea and the broader Asian region, and it may have been involved in several multinational collaborative projects. The Technical University of Munich, with seven published articles, ranks fifth. As one of Germany’s representative academic institutions, it possesses a strong research foundation and practical capabilities in the study of construction robotics. From the co-authorship network, it is evident that the Technical University of Munich collaborates with the Swiss Federal Institutes of Technology Domain and ETH Zurich, highlighting its academic prominence within the European construction robotics field.

The influence in the field of institutions such as the Swiss Federal Institutes of Technology Domain, Pennsylvania State University, Hong Kong Polytechnic University, ETH Zurich, and the Pennsylvania Commonwealth System of Higher Education (PCSHE), each with six published articles, may not be as large as that of the top institutions, but they still hold significant positions in the academic research.

From the distribution of the number of publications and the collaboration networks, it is evident that construction robotics research is showing a clear trend toward internationalization [32]. Leading research institutions are spread across North America, Europe, and Asia, contributing to a global research landscape.

In conclusion, the dominance of the University of Michigan in publication output aligns with its historical focus on human–robot collaboration. However, the sparse cross-border collaborations contrast sharply with the globalized research and development trends in manufacturing robotics. This paradox suggests that construction robotics remains siloed by regional policy barriers despite its technical potential for global scalability. To address this, we propose adopting open innovation frameworks to incentivize knowledge sharing across institutions. These institutions’ work has not only had a profound impact within academia, but has also driven technological advancements and applications within the industry. With the continuous development of construction robotics technology, the importance of regional cooperation networks is increasing. Notably, the close collaboration between research institutions in Asia, Europe, and North America is facilitating the integration and progress of cross-national technologies in this field. As construction robotics technology continues to advance, future research will likely focus more on smart, automated construction technologies and the sustainable development of the construction industry [33]. Interdisciplinary collaboration and the application of these technologies will be important trends in future research.

3.3. Visualization Analysis of Countries

As shown in

Table 4. Top 10 countries by number of publications.

No.	Country	Publication Volume	No.	Country	Publication Volume
1	People’s Republic of China	67	6	Canada	9
2	USA	65	7	Switzerland	7
3	South Korea	29	8	England	6
4	Germany	15	8	Australia	6
5	Japan	10	8	The Netherlands	6

. In the ranking of the number of publications by country, the People’s Republic of China (PEOPLES R CHINA) ranks first with 67 publications, indicating that China has been highly active in construction robotics research. In recent years, China’s increasing demand for automation and smart technologies in the construction industry has driven the rapid development of related technologies [34]. China’s research in construction robotics not only covers improvements to traditional construction techniques, but also explores the application of robots in areas such as construction waste recycling and construction site safety [35]. Chinese scholars and research institutions have become increasingly important in the global construction robotics field, particularly in driving the digital transformation of the construction industry and contributing to the development of green building practices [36].

Following closely is the USA, with 65 publications. The United States has a long history of research in construction robotics and has strong capabilities in cutting-edge technological fields such as robot autonomy, BIM, and AI. Leading research institutions and universities, such as MIT and Stanford, have played a dominant role in technological innovations and academic output in the field of construction robotics. The USA has not only advanced basic research in construction robotics, but has also achieved many breakthroughs in practical applications.

South Korea ranks third, with 29 publications. South Korea is renowned for its strong manufacturing base and high-level robotics technology development. The country has made significant progress in the application and innovation of construction robotics, particularly in areas such as robot operating systems and intelligent construction equipment.

Germany, with 15 publications, ranks fourth, highlighting its importance in the construction robotics field. Germany has a solid foundation in industrial automation and robotics technology, with strong governmental support for the research and development of construction robotics. German research mainly focuses on the integration of construction robotics with smart manufacturing and digital building technologies.

Countries like Canada, Switzerland, England, Australia, and the Netherlands, though they have fewer publications, still make significant contributions to the field of construction robotics. Switzerland, the Netherlands, and Canada in particular have strong research foundations in robotics technology and actively drive the global development of construction robotics through multinational collaborations. Institutions like ETH Zurich in Switzerland and other relevant research organizations in the Netherlands are closely involved with the global academic community and play an important role in the construction robotics field.

From the number of publications of these countries, it is clear that construction robotics research has become a global focus. As shown in Figure 3. China and the United States are the leaders in construction robotics research, with both countries producing the most research output and leading the field in technology application. Additionally, South Korea, Germany, and Japan have strong advantages in the development and application of construction robotics technology. The cooperation between China and the United States is particularly close, especially in technology innovation and applied research, with frequent collaborative exchanges between scholars and institutions from both countries. At the same time, European countries such as Germany and Switzerland have played significant roles in integrating construction robotics with smart buildings and BIM technologies. South Korea and Japan have rich research outcomes in high-performance construction robots and construction automation.

3.4. Visualization Analysis of Journal Distribution

Based on the journal co-occurrence diagram and the data from the top ten most productive journals, it can be seen that research in the field of construction robotics is primarily concentrated in a few key journals, exhibiting a clear trend of domain clustering. As shown in

Table 5. Top 10 journals by number of publications.

No.	Journal	Publication Volume	No.	Journal	Publication Volume
1	AUTOMAT CONSTR	160	6	J COMPUT CIVIL ENG	62
2	ADV ENG INFORM	75	7	J BUILD ENG	61
3	J CONSTR ENG M	72	8	AUTON ROBOT	59
4	IEEE INT CONF ROBOT	65	9	ROBOT CIM-INT MANUF	42
5	IEEE INT C INT ROBOT	64	10	J MANAGE ENG	39

. Automation in Construction (AUTOMAT CONSTR) is the journal with the highest number of publications, with 160 articles, which far surpasses other journals. This journal focuses on automation technologies in the construction process, including construction robots, building automation systems, and more, making it a leading platform for theoretical research and technical applications in this field. The large number of articles published in this journal reflects the broad attention construction robotics research is receiving, especially in areas such as automated construction, smart buildings, and BIM.

Advanced Engineering Informatics (ADV ENG INFORM) ranks second, with 75 articles published, highlighting its importance in the construction robotics and related fields. This journal covers topics in engineering informatics, including data analysis, the application of information technology in engineering, and more. As a high-tech application, construction robotics is widely studied in this journal. With the growing demand for digitization and informatization in the construction industry, this journal provides a significant platform for the integration of advanced technologies in construction.

Journal of Construction Engineering and Management (J CONSTR ENG M) ranks third, with 72 articles published. This journal focuses on the management and practice of construction engineering, covering aspects such as construction management and the coordination and scheduling of building projects. As the construction industry transitions toward smarter management, the application of construction robotics is gradually penetrating construction management, further emphasizing the importance of automation and robotics in this area.

In summary, research in the field of construction robotics is mainly concentrated in areas such as automation, robot control, construction management, and information technology. High-impact journals in these fields provide essential platforms for the exchange and development of these technologies. As shown in Figure 4. The distribution of journals and publication trends show that research in construction robotics is moving toward more efficient and intelligent solutions, gradually integrating with practical application areas such as engineering management and construction [37]. With the continuous advancement of technology, future research is likely to focus more on the integration of construction robotics with concepts like smart buildings and green construction, driving the overall intelligent transformation of the construction industry [38].

3.5. Visualization Analysis of Keywords

(1) Keyword Co-Occurrence

Keywords encapsulate the main research themes, key content, and technical methods of articles, providing a refined overview of the authors’ research focus. Analyzing the knowledge network of keywords helps identify specific research content and trends within a field. In this study, keywords were used as network nodes, and Pathfinder was applied for network path finding and pruning to generate a keyword co-occurrence network, as shown in the Figure 5. Specific keyword information is provided in the

Table 6. Top 9 high-frequency keywords in the research.

No.	Keyword	Count	Centrality
1	construction robot	34	0.38
2	construction robotics	33	0.27
3	system	31	0.27
4	construction robots	21	0.19
5	poverty	17	0.17
5	design	17	0.14
7	automation	16	0.12
8	human–robot collaboration	13	0.12
9	model	12	0.08

.

A total of 313 co-occurring keywords were identified in the research, excluding keywords with ambiguous meanings. The top 10 most frequently co-occurring keywords were “construction robot”, “construction robotics”, “system”, “construction robots”, “poverty”, “design”, “automation”, “human–robot collaboration”, and “model”. “Construction robot” and “construction robotics” can be seen as different expressions of the same theme, appearing 34 and 33 times, respectively, indicating that they are among the most important research topics in the construction robotics field, frequently appearing in the related literature.

The centrality of “construction robot” is 0.38, while “construction robotics” has a centrality of 0.27, both of which reflect their significance within the academic network. The higher centrality of “construction robot” suggests that it connects more subfields in academic discussions and may serve as a bridge to other technologies and applications, such as “automation”, “design”, and “system”. In construction robotics, the high frequency of the keyword “system” (31 occurrences) indicates the importance of system integration in the research of construction robots. Whether hardware or software systems, construction robots are complex systems composed of multiple components, and their design, implementation, and optimization have a wide-reaching impact. The centrality of 0.27 for “system” shows that it is an indispensable element in construction robotics, connecting multiple research areas, such as robot control systems and construction management systems. “Poverty” appears relatively infrequently (17 occurrences, centrality 0.17), but its inclusion in research related to construction robots suggests a direction focused on the application of construction robotics in developing or impoverished regions, advancing the modernization of the construction industry and improving living conditions. Its appearance thereby indicates the social benefits of construction robotics, particularly in reducing poverty and providing job opportunities. The low centrality (0.17) suggests that “poverty” has a weaker connection within the construction robotics research network, but it offers an important perspective on the social impact of the field.

“Design” appears 17 times, with a centrality of 0.14, underscoring the foundational role of design in the development of construction robotics. From hardware design to system design and operational process design, the frequent appearance of “design” reflects its importance in the field. The centrality of 0.14 indicates that this theme is strongly connected to related fields, although its influence is somewhat weaker compared to other keywords such as “construction robot”.

The keyword analysis shows that future research in construction robotics will increasingly focus on multidisciplinary integration. The development of robot technology (e.g., mechanical design, sensor technologies, AI) will closely align with automation and system optimization in the construction process, driving the digital and intelligent transformation of the construction industry. As the research deepens, keywords such as “human–robot collaboration”, “automation”, and “design” indicate that the field is moving towards greater intelligence and integration, gradually incorporating more interdisciplinary technologies. The ability to improve human–robot collaboration, enhancing efficiency while ensuring construction safety, will be a key direction for the further development of construction robotics technology. Although “poverty” appears less frequently, it still reveals the social impact of construction robotics, especially in enhancing construction efficiency and improving living conditions in impoverished regions.

(2) Keyword Clustering

As shown in Figure 6, it is clear that the research topics in construction robotics are diverse and strongly interconnected. Below is an analysis of the main clusters, discussing the key themes and development trends in the field, in conjunction with current international academic research [23].

Cluster 0 focuses on construction robots and construction robotics. The keywords include “construction robot”, “robotic transport mechanism”, “automated construction”, and “force feedback”. This cluster centers on the fundamental concepts and applications of construction robots. The research emphasizes how robotics technology is applied in construction tasks, such as transportation and automation. It highlights the design of robotic hardware, automation control, and the enhancement of robots’ performance in construction environments.

Cluster 1 revolves around simultaneous localization and mapping (SLAM), with keywords such as “simultaneous localization”, “construction robots”, “mapping”, and “large-scale manipulator”. This cluster focuses on the spatial localization and mapping technologies used in construction robots. SLAM technology is crucial for robots, especially in construction settings, where robots need to adjust their position and actions based on real-time environmental information. Research in this area concentrates on how SLAM can enhance the autonomous navigation capabilities of construction robots, particularly in complex environments, and support large-scale construction tasks.

Cluster 3 explores the broader field of technology, with keywords such as “technology”, “construction industry”, and “robotics technology”. This cluster reflects the connections between construction robotics and various technological advancements. It includes the integration of robotics within the construction industry, examining the application of robots in different construction activities. The main research direction is integrating emerging technologies like artificial intelligence, sensors, and data processing into construction robot systems, thereby enhancing the intelligence and efficiency of these robots.

The prominence of “human–robot collaboration” (cluster 4) validates Bock’s (2015) prediction of hybrid workflows but exposes a critical gap: current studies prioritize technical feasibility over socio-economic impacts. For instance, while Zhu et al. (2023) optimized robotic assembly through deep reinforcement learning, few other studies have addressed how such systems may displace low-skilled workers. This misalignment underscores the need for transdisciplinary research that integrates robotics, labor economics, and policy design—a direction scarcely explored in the existing literature. This cluster addresses how robots work together with human workers in construction settings. The research is centered on improving the efficiency of collaboration, reducing conflicts, and increasing the adaptability of robots. Future studies will likely concentrate on enhancing human–robot collaboration through intelligent systems, particularly in complex construction environments where efficient interaction and coordination are critical.

Cluster 5 is concerned with masonry quality, featuring keywords like “masonry quality”, “robotic assembly”, and “response surface methodology”. This cluster primarily investigates the application of construction robots in masonry and brickwork, particularly their contributions to improving construction quality and precision. Future research in this cluster will likely explore how robotics can improve material accuracy and construction quality, reducing the errors typically introduced by manual labor.

Cluster 6 is focused on underwater robots, with keywords such as “underwater robots”, “mobile robots”, and “construction robots”. While less directly connected to traditional construction tasks, this cluster provides a new perspective for expanding the applications of construction robotics. The development of underwater robots could play a crucial role in supporting construction projects in specialized environments, such as underwater, with robotics being utilized for tasks in such challenging settings.

Cluster 7 discusses mechanism design, with keywords like “mechanism design”, “robot systems”, and “mobile robots”. This cluster covers the mechanical principles and system design of construction robots, focusing on how to optimize robot performance, improve efficiency, and ensure safety in construction. Research in this area focuses on designing more efficient and flexible systems for construction robots, especially in complex environments where the robots’ adaptability and reliability are critical.

Cluster 8 delves into behavioral and cognitive sciences, with keywords such as “behavioral and cognitive sciences”, “robotics”, and “self-organization”. This cluster emphasizes the application of cognitive and behavioral sciences in construction robotics, particularly in enabling robots to learn and adapt. The research here aims to enhance construction robots’ self-adaptation and decision-making abilities, enabling them to work more effectively in dynamic and evolving environments.

Finally, cluster 9 is focused on accuracy, with keywords like “accuracy”, “measurement”, and “dimensional analysis”. Accuracy is a critical issue in construction robotics, especially when dealing with tasks that require precise measurements and data analysis. Research in this area focuses on improving the precision of construction robots in complex construction tasks, particularly in high-precision measurements and modeling during construction projects.

Through the clustering analysis, it is evident that construction robotics is evolving towards diversification and greater intelligence. Whether it involves basic mechanical design, robot system integration, human–robot collaboration, or technological innovation, the field of construction robotics is seeing widespread academic interest and in-depth discussions. Future research will likely emphasize the integration of interdisciplinary technologies and the practical applications of these innovations, further driving the modernization and intelligent transformation of the construction industry [39]. The continued development of human–robot collaboration and automation will be crucial in making construction processes more efficient, safe, and adaptable to diverse environments [40].

(3) Keyword Timeline and Burst

The timeline diagram reveals the evolving trajectory of research in the field of construction robotics [41]. As shown in Figure 7. It shows that the focus of various topics has shifted over time, with certain milestones and trends emerging at key points [42].

Keywords such as “construction robot” and “construction robotics” have been frequently cited since 2004, and drew significant attention from 2005. As seen in the timeline, “construction robot” gained substantial citations early on (from 2004 to 2005), and its prominence has continued to grow in the field over time. It can be inferred that the study of construction robots began with early technological explorations, and, as robotic technology advanced, more scholars began to focus on its applications in the construction industry. Between 2008 and 2020, several keywords, such as “construction automation”, “BIM”, and others, highlight the progress in research related to automation, information modeling, and system integration. With the growing demand for automation and intelligent technologies in the construction industry, research shifted from specific robotic applications to a broader focus on global system design, information management, and other technical aspects.

The post-2020 surge in “cognitive load” research reflects a shift toward human-centric automation, but this trend remains largely confined to academic prototypes. For example, Adami et al. (2021) demonstrated VR’s efficacy in training workers, but real-world adoption lags due to high implementation costs. This gap between innovation and application echoes Thaker’s (2021) critique of ‘techno-optimism’ in construction robotics—a challenge our study highlights as a priority for industry–academia partnerships. This indicates that scholars have started to explore how advanced technologies like AI and virtual reality can enhance the intelligence and functionality of construction robots. The keyword “cognitive load” specifically shows how construction robots might self-regulate during high-intensity work, optimizing human–robot collaboration. The emergence of the “safety” keyword in 2022, with continued growth in 2023, reflects the increasing focus on improving safety on construction sites using robotic technology. Robots can reduce the need for human intervention in high-risk tasks, thereby improving safety. The rapid prominence of “virtual reality” shows that the integration of construction robotics with virtual environments is becoming a new research hotspot. This could involve training construction workers, conducting virtual construction simulations, or enhancing human–robot interaction experiences. Additionally, the keyword “human–robot interaction”, highlighted in 2023, demonstrates the growing importance of effective communication and collaboration between humans and robots as robotic technology matures.

As shown in Figure 8. Based on the trends observed in recent years, future research on construction robotics is likely to focus more on the following areas: (1) Intelligence: Keywords such as “virtual reality” and “cognitive load” suggest that future research will be centered on improving the intelligence of robots, allowing them to adapt to complex environments and collaborate more effectively with human workers [43]. (2) Safety and efficiency: With the increasing demand for safety in the construction industry, learning how to use robotics to reduce safety hazards and enhance construction efficiency will be a key research direction [44].

Based on the analysis of the timeline and keyword highlight diagrams, it is clear that research in the field of construction robotics is undergoing a transition from basic technologies to more intelligent and systematized developments. Early research primarily focused on the hardware and fundamental functions of construction robots, while, over time, scholars have shifted their attention to how human–robot collaboration, information modeling, automation, and intelligent technologies can enhance the efficiency, safety, and scope of applications of construction robots [45]. Future research trends are likely to focus more on improving construction robots’ adaptability to real-world environments, particularly how intelligent technologies can optimize collaboration and management in construction processes, thereby driving the comprehensive intelligent transformation of the construction industry [18].

4. Discussion

4.1. History and Trends

This study offers a comprehensive and nuanced analysis of recent advancements in construction robotics through a bibliometric analysis. Unlike previous literature reviews, which often focused narrowly on specific applications or regional developments, this work adopts a global perspective. This work integrates interdisciplinary technologies and their impacts on the construction industry, providing a more holistic understanding of how AI, BIM, and sensor technologies have collectively shaped the evolution of construction robotics [46].

The differences of this work compared to earlier studies can be attributed to several factors. Firstly, this work incorporates a broader range of data sources, ensuring a more representative sample of global research efforts. This comprehensive approach allowed us to capture trends and developments that might have been overlooked in previous studies. Secondly, this work’s focus on interdisciplinary integration highlights how advancements in AI and sensor technologies have accelerated the development of construction robotics, a dimension less emphasized in previous reviews [19]. Additionally, this methodology allowed us to identify emerging trends, such as the increasing attention paid to human–robot collaboration and virtual reality applications, which were under-represented in earlier analyses [47].

In the context of the construction robot industry, the phenomena of market adoption barriers and economic feasibility can also be analyzed using the content of the academic literature. For example, in terms of investment trends, global venture capital funding in construction robotics has surged, driven by labor shortages and sustainability mandates; in terms of policy support, analyses of government initiatives (e.g., EU’s Horizon 2020 robotics grants, China’s “New Infrastructure” policy) have accelerated their commercial adoption; and, in terms of cost–benefit challenges, high upfront costs remain a barrier for small and medium enterprises (SMEs), necessitating modular and leasing models.

Despite these strengths, this study has certain limitations. One notable gap is the limited exploration of economic factors influencing the adoption of construction robotics, particularly the high initial costs and maintenance expenses that hinder implementation in smaller firms. Future research should address this by examining cost–benefit analyses and developing strategies to make these technologies more accessible to firms with limited budgets. Another limitation is the relatively static nature of bibliometric analysis, which may not fully capture the dynamic and rapidly evolving nature of technological advancements in real-time applications [48]. To address these gaps, future research should prioritize the following:

(1)

Extreme environment applications: Deploying robots in underwater construction (e.g., building bridge foundations) or nuclear facility maintenance, leveraging advancements in sensor fusion and autonomous navigation.

(2)

Green construction integration: Aligning with sustainability goals, such as AI-guided material recycling robots for circular economy practices.

(3)

Global collaborative frameworks: Establishing cross-border consortia to standardize protocols (e.g., ISO certifications for swarm robotics) and sharing best practices.

(4)

Human-centric AI: Developing “cognitive load” mitigation systems to enhance human–robot collaboration in high-risk tasks like high-rise welding.

By bridging these gaps, the field can transition from fragmented innovation to holistic progress, ultimately realizing the vision of Construction 5.0.

4.2. Beyond Bibliometrics: Translating Trends into Action

While our analysis identifies AI and modular robotics as dominant trends, their real-world impact hinges on resolving the following three tensions: (1) Technological vs. social readiness: High-income nations lead in AI-driven innovations, yet low-resource regions face infrastructure gaps that limit robotic adoption. (2) Automation vs. employment: Despite claims of labor shortage mitigation, rapid automation may exacerbate inequality without reskilling programs. (3) Standardization vs. flexibility: Modular systems promise scalability but risk stifling customization, a trade-off requiring context-sensitive design frameworks. These tensions demand a policy-aware research agenda, bridging the technical focus of robotics with sustainable development goals.

5. Conclusions

This study provides novel insights into the field of construction robotics by systematically mapping global research trends and identifying critical gaps in interoperability, ethical governance, and human–robot collaboration. Through bibliometric analysis, this work reveals the dominance of hardware-centric innovations and the under-representation of adaptive AI-driven solutions, underscoring the need for interdisciplinary synergy. Future research can build upon these findings through focusing on the following areas: (1) The economic aspects of construction robotics, including cost–benefit analyses and strategies for wider adoption. (2) The integration of emerging technologies such as blockchain and IoT to improve supply chain management and site monitoring. (3) The expansion of the application of construction robotics to extreme environments, driving innovation in robot design and autonomous decision making. By integrating these advancements with emerging technologies such as quantum computing and bio-inspired robotics, the construction industry can achieve scalable, sustainable automation, ultimately bridging the gap between academic innovation and industrial adoption.